Cloud computing (CC) is the fastest-growing data hosting and computational technology that stands today as a satisfactory answer to the problem of data storage and computing. Thereby, most organizations are now migratingtheir services into the cloud due to its appealing features and its tangible advantages. Nevertheless, providing privacy and security to protect cloud assets and resources still a very challenging issue. To address the aboveissues, we propose a smart approach to construct automatically an efficient and effective anomaly network IDS based on Deep Neural Network, by using a novel hybrid optimization framework “ISAGASAA”. ISAGASAA framework combines our new self-adaptive heuristic search algorithm called “Improved Self-Adaptive Genetic Algorithm” (ISAGA) and Simulated Annealing Algorithm (SAA). Our approach consists of using ISAGASAA with the aim of seeking the optimal or near optimal combination of most pertinent values of the parametersincluded in building of DNN based IDS or impacting its performance, which guarantee high detection rate, high accuracy and low false alarm rate. The experimental results turn out the capability of our IDS to uncover intrusionswith high detection accuracy and low false alarm rate, and demonstrate its superiority in comparison with stateof-the-art methods.

Digital transmission systems carry information from the source to the receiver using a physical medium such as cable, fiber optic or even propagation on a radio channel which isn't entirely reliable and causes the change of data originally emitted. Today the use of error correcting codes for protection and correction becomes an integral part in the design of communication systems and computers. In this work, we present a new interesting way to accelerate the decoding process of linear codes. The proposed method called Soft Decision Decoder by Hash Techniques (SDHT) is based on syndrome-decoding algorithm and hash techniques. The use of this latest allows reducing considerably the search time of all possible error patterns of weights less than a fixed threshold. SDHT is applicable on many linear codes and exploit the polynomial form to reduce again the run time decoding for cyclic codes. SDHT is successfully applied to decode some Bose Ray-Chaudhuri and Hocquenghem (BCH), Quadratic Residue (QR) and Extended Quadratic Residue (EQR) codes. The simulation results show that the proposed SDHT yield to good error correcting performances with reduced complexity. The comparison between SDHT and many competitors shows that it gives better performances in terms of correction rate. The experimental study of the decoding steps for the BCH(63,45,7) code shows that the time search of the most likely error pattern is reduced at about 26214153% comparing to an exhaustive search of all possible error patterns of weights less than or equal to 4. This study proves the huge success of the proposed SDHT decoder.

Quadratic Residue codes are among the best codes. They have high capacity of error correction but they are very difficult to enumerate and therefore to analyse. Despite all developed methods in this domain, the weights enumerators of Quadratic Residue codes are known only for lengths less than or equal to 167. For the lengths 191 and 199 only estimations are available. In this paper, we present a new method based on the Multiple Impulse Method (MIM) and hash techniques to find the weights enumerators of Quadratic Residue codes having lengths in the form 8m-1, for an integer m. The proposed method Hash_MIM_Weights_Enumerators is validated on all Quadratic Residue codes of known weights enumerators; its reduced spatial and temporal complexities yields to new important results. So, the weights enumerators for the lengths 191, 199 and 223 are determined. These three codes are the best binary linear block codes in terms of minimum distance known until today and their analytical performances are remained unknowns in more than 60 years ago and they are available now.

Abstract-BCH codes have high error correcting capability which allows classing them as good cyclic error correcting codes. This important characteristic is very useful in communication and data storage systems. Actually after almost 60 years passed from their discovery, their weights enumerators and therefore their analytical performances are known only for the lengths less than or equal to 127 and only for some codes of length as 255. The Partial Weights Enumerator (PWE) algorithm permits to obtain a partial weights enumerators for linear codes, it is based on the Multiple Impulse Method combined with a Monte Carlo Method; its main inconveniece is the relatively long run time. In this paper we present an improvement of PWE by integration of Hash techniques and a part of Automorphism Group (PWEHA) to accelerate it. The chosen approach applies to two levels. The first is to expand the sample which contains codewords of the same weight from a given codeword, this is done by adding a part of the Automorphism Group. The second level is to simplify the search in the sample by the use of hash techniques. PWEHA has allowed us to considerably reduce the run time of the PWE algorithm, for example that of PWEHA is reduced at more than 3900% for the BCH (127,71,19) code. This method is validated and it is used to approximate a partial weights enumerators of some BCH codes of unknown weights enumerators.